Enabling policy environment for water, food and energy security
Bibliographic record
Abstract
Abstract The complexity of water, food and energy security is analysed from the perspectives of (i) water and food and (ii) water and energy and their interconnectivity and focuses ultimately on water as a primary input into processes, the entry point for participants of the Third World Irrigation Forum. The paper provides an overview of trends in water, food and energy security, highlights the interconnectivity between the various elements and introduces the water–food–energy nexus as a tool for improving productivity and sector policies, avoiding unintended consequences on other sectors. Invariably, there will be trade‐offs and the challenge is to find combinations of measures that have a net positive outcome. In order to quantify security in the three elements and the trade‐offs between them, emerging modelling approaches for the nexus are discussed. Sub‐theme 3 of the forum focuses on productivity and technology interventions 1 and sub‐theme 2 on stakeholder interaction. The combination of modelling, technology innovations and stakeholder participation in a water–food–energy nexus approach leads to better understanding of linkages and more robust policies and is used to derive recommendations for an enabling policy environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".